The present embodiments relate to medical diagnostic imaging. Many decisions in modern cardiology are based on quantitative measurements of anatomy, non-invasively derived from non-invasive imaging. Dimensions of the heart are different in normal function compared to open heart surgery.
Quantifying planar structures on two-dimensional (2D) images with contouring tools requires time consuming and diligent manual outlining. Convenient user interaction is important, particularly in an interventional setting where there may be limited degrees of freedom due to the many different activities occurring and/or limited user interface available in that setting (e.g., joystick control).
Recent advances in scanner technology enable three-dimensional plus time (3D+t) real-time ultrasound imaging of the heart. 3D imaging may make quantification even more difficult. 3D imaging is recognized to provide a better understanding of anatomical shape compared to traditional 2D imaging. On the other hand, the complexity of operating 2D ultrasound imaging is lower than 3D and often preferred in clinical practice. Also, for anatomical quantification, measurements are performed on 2D multi-planar reformatted or reconstruction (MPR) images, which are selected with additional user interfaces such as trackball controls or table side joystick controls.
With machine learning technologies, regular anatomical structures are efficiently and robustly modeled in fully-automatic or semi-automatic ways. Tackling anatomical variability may however be challenging, particularly in exceptional cases. Moreover, the machine-learnt technology is trained to provide specific segmentation and/or measurements, but users may want to define custom dimensions depending on their needs. Efficient workflows for deriving generic arbitrary quantitative information from 3D images in fast intuitive ways remain to be defined.